Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.

Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.

Is Increased Intracranial Pressure a Factor in Persistent Headache After Coronavirus Disease 2019?

ABSTRACT: This study aimed to determine pain characteristics in patients with persistent headache after COVID-19 and to investigate the role of increased intracranial pressure (ICP) in the pathogenesis of this headache. This is a case-control study comparing the parameters and measurements indicating increased ICP based on magnetic resonance imaging between COVID-19-diagnosed patients with persistent headache and a control group. Optic nerve sheath diameter (ONSD) and eyeball transverse diameter (ETD) were performed on the left eye of each participant. Seventeen of the patients (53.12%) met the diagnostic criteria for new daily persistent headache. Seven patients (21.87%) had migraine, and eight (25%) had tension headache characteristics. No significant difference was observed between the patient and control groups in terms of the ONSD and ETD values. It is possible that the etiopathogenesis is multifactorial. We consider that future studies that will evaluate ICP measurements in large patient groups can present a different perspective for this subject.